Every year, Spotify releases an end of the year “Spotify Wrapped”, which is a collection of stats regarding your listening habits over the past year. A few friends and I felt like this year’s stats were a little fishy, or at least, weren’t entirely expected. For example, my top artist of the year in 2022 was Pinegrove, however, I feel like I didn’t actually listen to Pinegrove all that frequently in the year 2022.
Spotify is not very forthcoming with regards to how they calculate their stats. For this reason, I wanted to investigate the data myself using the quantitative modeling skills that I acquired during my horribly expensive MPH.
There are a couple of routes one could take to explore their Spotify
data. There is the spotifyR package, which is a Spotify API
wrapper for R. Alternatively, one could download their data
directly from Spotify and then work with it directly.
Here’s the short of it: Spotify uses a lot of weird terminology when
it comes to how they code their API, which made it difficult to parse
the stats using spotifyR. The Spotify API only allows you
to make 50 queries at a time, and I can imagine that over the lifetime
of my account I’ve streamed multiple hundreds of thousands of songs. For
this reason, I have focused on the downloadable data for now.
Let’s delve into it! Shoot me a message if you want help downloading your Spotify data.
I’ve made a master script which I use to source all of my functions.
This keeps things tidy. If you’d like to run this on your own, first
make sure to call source() on wherever you’ve placed the
spotRfy_master.R script. Then, you can use the
get_spotify_streaming_data() function to create a data
frame of the streaming data from your downloaded and extracted Spotify
data (make sure to change the datafile file path to
wherever your data was downloaded). Then, run the data through the
clean_spotify_streaming_data() function and specify your
local timezone. By default, the timezone is PST.
# source("../scripts/spotRfy_master.R")
myStreamingData <- import_spotify_streaming_data(datafile = "../data/lasty")
myCleanData <- clean_spotify_streaming_data(data = myStreamingData, your_timezone = "EST")Now we have a data set that is ready for plotting.
My primary interest was in visualizing when and what I listened to
over the past year. I wrote the plot_streaming_timeofday()
function, which creates a ggplot/plotly object that is interactive,
allowing you to turn certain data on and off. It also allows you to
hover over individual points to see what song/artist you were listening
to.
plot_streaming_timeofday(myCleanData, artist_cutoff = 10)By default, the plot only highlights music artists that encompass
your top 10 most listened to artists (by total minutes listened). This
is because it becomes very difficult to interpret the plot while
highlighting all of the artists you’ve ever listened to. I
found that the top 10 was a good amount. If you would like to change it
to a different cutoff, set the variable cutoff equal to the
desired number of artists (i.e. cutoff = 15 for your top 15
artists).
If you click on an artist (or on “Artists Under Top Cutoff”) you can remove that artist from the overall plot. If you double click on an artist, you can isolate that artist’s data on the plot.
I have plans to incorporate a slider at the bottom of the plot which will allow you to change the time frame. Additionally, I would like to include album data, however that will take integration with the Spotify API and I haven’t written the function to do that as of yet.
Interestingly, you can see my sleep/wake habits from the data (note how they shifted around ~July when I moved from the East Coast to the West Coast). You can also see the night that I played white noise to down out my loud neighbors (on 10/22/22, shout out to Baby Sleep Noise 5).
I wanted to visualize how each artist’s song contributed to the
overall listening time for that artist. To do this, I wrote the
plot_streaming_artists() function. This visualizes the
total amount of time listened to each artist. If you hover over the
bars, you can see how much each song from that artist contributed to the
overall time listened to said artist.
plot_streaming_artists(myCleanData, artist_cutoff = 10)Turns out I did listen to a lot of Pinegrove this year!
Similar to the previous function, if you would like to plot a greater
number of artists, you can change the cutoff variable.
I would like to include the ability to color by album, however that will need to be done in the future when I integrate the API.
I would like to write a function that downloads the metadata of the songs that you’ve listened to from the Spotify API, but that is proving to be tricky and may not be easy to implement for everyone.
If you have other ideas for how I could improve the code, let me know!